from enum import Enum
from typing import Optional

from pydantic import BaseModel
from pydantic import Field


class DifficultyTier(str, Enum):
    easy = "easy"
    medium = "medium"
    hard = "hard"


class Action(BaseModel):
    command: str = Field(min_length=1)
    reasoning: Optional[str] = None


class Observation(BaseModel):
    stdout: str
    stderr: str
    exit_code: int
    working_directory: str
    execution_time: float = Field(ge=0.0)
    reward: float
    done: bool
    step_number: int = Field(ge=0)
    max_steps: int = Field(gt=0)
    # optional progress signals populated by the server-side reward engine.
    # clients that care about shaped progress (training) read these. older
    # clients simply ignore them.
    grader_health: float = 0.0
    grader_details: dict[str, bool | float | str] = Field(default_factory=dict)
    ood_http_code: str = ""


class EnvironmentState(BaseModel):
    episode_id: str = Field(min_length=1)
    task_id: str = Field(min_length=1)
    step_count: int = Field(ge=0)
    max_steps: int = Field(gt=0)
    done: bool
    reward: float


class ResetRequest(BaseModel):
    task_id: Optional[str] = None


class StepRequest(BaseModel):
    action: Action
    # optional episode id so concurrent rollouts don't clobber each other's
    # session. older clients that omit it fall back to the most recently
    # created episode on the server.
    episode_id: Optional[str] = None


class StepResult(BaseModel):
    observation: Observation
    state: EnvironmentState


class TaskMetadata(BaseModel):
    task_id: str = Field(min_length=1)
    difficulty: DifficultyTier
    description: str
    max_steps: int = Field(gt=0)
    time_limit: float = Field(gt=0.0)
    base_filesystem_path: str


class RewardSignal(BaseModel):
    health_delta: float
    knowledge_delta: float = Field(ge=0.0)
    action_penalty: float = Field(le=0.0)
    total_reward: float


class DiagnosticTrigger(BaseModel):
    fact_id: str = Field(min_length=1)
    command_patterns: list[str] = Field(min_length=1)
    reward: float = Field(gt=0.0)


class TaskScenarioState(BaseModel):
    health: float = Field(ge=0.0, le=1.0)
    done: bool
    details: dict[str, bool | float | str]


class TaskScenarioDefinition(BaseModel):
    metadata: TaskMetadata
    requires_network_isolation: bool = True
    allows_nested_sandbox: bool = False
    diagnostic_triggers: list[DiagnosticTrigger] = Field(default_factory=list)